Optimizing Brain Tumor MRI Classification With Transfer Learning: A Performance Comparison of Pre-Trained CNN Models
DOI:
https://doi.org/10.23887/janapati.v14i1.87377Keywords:
Brain Tumor Detection, Convolutional Neural Network (CNN), Transfer Learning, VGG19, ResNet50, InceptionV3, DenseNet121Abstract
This study aims to classify brain MRI images into several types of brain tumors using the Convolutional Neural Network (CNN) approach with transfer learning. This method has the advantage of processing complex images in a shorter time than conventional CNN approaches. In this study, the data used was a public database from Kaggle, which consisted of four categories: glioma, meningioma, no tumor, and pituitary. Before entering the transfer learning process, data augmentation is carried out on the training data. Four pre-trained CNN models were used: VGG19, ResNet50, InceptionV3, and DenseNet121. The four models compared their ability to classify MRI images with several evaluation metrics: accuracy, precision, recall, and F1 score. The results of the performance comparison of the four pre-trained models show that the ResNet50 is the best model, with an accuracy of 98%. Meanwhile, VGG19, DenseNet121, and InceptionV3 produce 97%, 96%, and 95% accuracy, respectively. The ResNet50 architecture demonstrated superior performance in brain tumor classification, achieving 98% accuracy. It can be attributed to its residual learning structure, which efficiently manages complex MRI features. Further research should concentrate on larger, more diverse datasets and advanced preprocessing techniques to enhance model generalizability.
References
V. Essianda, A. D. Indrasari, P. Widyastuti, T. Syahla, and R. Rohadi, “Brain Tumor : Molecular Biology, Pathophysiology, and Clinical Symptoms,” J. Biol. Trop., vol. 23, no. 4, pp. 260–269, 2023, doi: 10.29303/jbt.v23i4.5585.
S. Lapointe, A. Perry, and N. A. Butowski, “Primary brain tumours in adults,” Lancet, vol. 392, no. 10145, pp. 432–446, 2018, doi: https://doi.org/10.1016/S0140-6736(18)30990-5.
J. Ferlay et al., “Global Cancer Observatory: Cancer Today,” Lyon, France: International Agency for Research on Cancer. Accessed: Aug. 13, 2024. [Online]. Available: https://gco.iarc.who.int/today
J. Park and Y. G. Park, “Brain Tumor Rehabilitation: Symptoms, Complications, and Treatment Strategy,” Brain & Neurorehabilitation, vol. 15, no. 3, 2022, doi: 10.12786/bn.2022.15.e25.
M. Pichaivel, G. Anbumani, P. Theivendren, and M. Gopal, “An Overview of Brain Tumor,” IntechOpen, 2022, doi: doi: 10.5772/intechopen.100806.
L. Hallundbæk et al., “Musculoskeletal misdiagnoses in children with brain tumors: A nationwide, multicenter case-control study,” PLoS One, vol. 18, no. 6 June, pp. 1–14, 2023, doi: 10.1371/journal.pone.0279549.
A. D. Dariansyah, W. Suryaningtyas, and M. A. Parenrengi, “Tuberculoma mimicking postoperative VP shunt seeding of craniopharyngioma: A rare case report,” 2021. doi: 10.25259/SNI_606_2021.
M. Celik and O. Inik, “Development of hybrid models based on deep learning and optimized machine learning algorithms for brain tumor Multi-Classification,” Expert Syst. Appl., vol. 238, no. PE, p. 122159, 2024, doi: 10.1016/j.eswa.2023.122159.
S. M. Alzahrani, “ConvAttenMixer: Brain tumor detection and type classification using convolutional mixer with external and self-attention mechanisms,” J. King Saud Univ. - Comput. Inf. Sci., vol. 35, no. 10, p. 101810, 2023, doi: 10.1016/j.jksuci.2023.101810.
A. Biswas and M. S. Islam, “A Hybrid Deep CNN-SVM Approach for Brain Tumor Classification,” J. Inf. Syst. Eng. Bus. Intell., vol. 9, no. 1, pp. 1–15, 2023, doi: 10.20473/jisebi.9.1.1-15.
Z. Rasheed et al., “Brain Tumor Classification from MRI Using Image Enhancement and Convolutional Neural Network Techniques,” Brain Sci., vol. 13, no. 9, 2023, doi: 10.3390/brainsci13091320.
V. K. Dhakshnamurthy, M. Govindan, K. Sreerangan, M. D. Nagarajan, and A. Thomas, “Brain Tumor Detection and Classification Using Transfer Learning Models,” p. 1, 2024, doi: 10.3390/engproc2024062001.
C. Srinivas et al., “Deep Transfer Learning Approaches in Performance Analysis of Brain Tumor Classification Using MRI Images.,” J. Healthc. Eng., vol. 2022, p. 3264367, 2022, doi: 10.1155/2022/3264367.
S. Tammina, “Transfer learning using VGG-16 with Deep Convolutional Neural Network for Classifying Images,” Int. J. Sci. Res. Publ., vol. 9, no. 10, p. p9420, 2019, doi: 10.29322/ijsrp.9.10.2019.p9420.
L. Ding, J. Peng, L. Song, and X. Zhang, “Automatically detecting apnea-hypopnea snoring signal based on VGG19 + LSTM,” Biomed. Signal Process. Control, vol. 80, p. 104351, 2023, doi: https://doi.org/10.1016/j.bspc.2022.104351.
I. L. Kharisma, R. T. Handayanto, and D. A. Dewi, “Face Mask Detection In The Covid-19 Pandemic Era by Implementing Convolutional Neural Network and Pre-Trained CNN Models,” in 2021 IEEE 7th International Conference on Computing, Engineering and Design (ICCED), 2021, pp. 1–6. doi: 10.1109/ICCED53389.2021.9664883.
L. Alzubaidi et al., Review of deep learning: concepts, CNN architectures, challenges, applications, future directions, vol. 8, no. 1. Springer International Publishing, 2021. doi: 10.1186/s40537-021-00444-8.
N. Nainan, J. H, R. Jalan, R. N, K. C, and S. Shankar, Real Time Face Mask Detection Using MobileNetV2 and InceptionV3 Models. 2021. doi: 10.1109/MysuruCon52639.2021.9641675.
X. Renjun, Y. Junliang, W. Yi, and S. Mengcheng, “Fault Detection Method Based on Improved Faster R-CNN: Take ResNet-50 as an Example,” Geofluids, vol. 2022, 2022, doi: 10.1155/2022/7812410.
S. Singha and P. Roy, “Skin Cancer Classification and Comparison of Pre-trained Models Performance using Transfer Learning,” J. Inf. Syst. Eng. Bus. Intell., vol. 8, no. 2, pp. 218–225, 2022, doi: 10.20473/jisebi.8.2.218-225.
G. Huang and K. Q. Weinberger, “Densely Connected Convolutional Networks”.
T. Le and V. M. Ngo, “EAI Endorsed Transactions Automated evaluation of Tuberculosis using Deep Neural Networks,” vol. 9, no. 30, pp. 1–14, 2022.
M. Nickparvar, “Brain Tumor MRI Dataset [Dataset],” Kaggle. Accessed: Aug. 14, 2024. [Online]. Available: https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset?resource=download
J. Cheng, “Brain Tumor Dataset [Dataset],” Figshare, 2017, doi: 10.6084/m9.figshare.1512427.v5.
S. Bhuvaji, A. Kadam, P. Bhumkar, S. Dedge, and S. Kanchan, “Brain Tumor Classification (MRI) [Dataset],” Kaggle, 2020, doi: https://doi.org/10.34740/KAGGLE/DSV/1183165.
A. Hamada, “Br35H :: Brain Tumor Detection 2020 [Dataset],” Kaggle, 2020, [Online]. Available: https://www.kaggle.com/datasets/ahmedhamada0/brain-tumor-detection
I. H. Witten and E. Frank, Data Mining Practical Machine Learning Tools and Techniques, 4th ed. Cambridge: Elsevier Inc, 2017. [Online]. Available: http://books.google.com/books?hl=en&lr=&id=QTnOcZJzlUoC&oi=fnd&pg=PR17&dq=Data+Mining+Practical+Machine+Learning+Tools+and+Techniques&ots=3gpDdrWiOc&sig=TZS7G8l1eXSa2SpAvfD6aBoJ2lw
S. E. Jozdani, B. A. Johnson, and D. Chen, “Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification,” Remote Sens., vol. 11, no. 14, 2019, doi: 10.3390/rs11141713.
M. W. A. Kesiman, I. M. G. Sunarya, and I. G. L. T. Sumantara, “Comparative Analysis of CNN Methods for Periapical Radiograph Classification,” J. Nas. Pendidik. Tek. Inform., vol. 13, no. 2, pp. 204–214, 2024, doi: 10.23887/janapati.v13i2.71664.
A. H. M. Linkon, M. M. Labib, T. Hasan, M. Hossain, and M.-E.- Jannat, “Deep learning in prostate cancer diagnosis and Gleason grading in histopathology images: An extensive study,” Informatics Med. Unlocked, vol. 24, p. 100582, 2021, doi: https://doi.org/10.1016/j.imu.2021.100582.
S. Matarneh, F. Elghaish, F. Pour Rahimian, E. Abdellatef, and S. Abrishami, “Evaluation and optimisation of pre-trained CNN models for asphalt pavement crack detection and classification,” Autom. Constr., vol. 160, p. 105297, 2024, doi: https://doi.org/10.1016/j.autcon.2024.105297.
G. Pinto, Z. Wang, A. Roy, T. Hong, and A. Capozzoli, “Transfer learning for smart buildings: A critical review of algorithms, applications, and future perspectives,” Adv. Appl. Energy, vol. 5, p. 100084, 2022, doi: https://doi.org/10.1016/j.adapen.2022.100084.
T. Mousami, S. R, K. Sadhvika, K. Sakshi, and K. Vaishnavi, “CNN Based Deep Learning Approach for Automatic Malaria Parasite Detection,” IAENG Int. J. Comput. Sci., vol. 49, no. 3, pp. 745–753, 2022.
A. Kolesnikov et al., “Big Transfer (BiT): General Visual Representation Learning,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020, pp. 491–507. doi: 10.1007/978-3-030-58558-7_29.
A. V. Ikechukwu, S. Murali, R. Deepu, and R. C. Shivamurthy, “ResNet-50 vs VGG-19 vs training from scratch : A comparative analysis of the segmentation and classification of Pneumonia from chest X-ray images,” Glob. Transitions Proc., vol. 2, no. 2, pp. 375–381, 2021, doi: 10.1016/j.gltp.2021.08.027.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2818–2826. doi: 10.1109/CVPR.2016.308.
W. Xu, Y.-L. Fu, and D. Zhu, “ResNet and its application to medical image processing: Research progress and challenges.,” Comput. Methods Programs Biomed., vol. 240, p. 107660, Oct. 2023, doi: 10.1016/j.cmpb.2023.107660.
A. Brock, S. De, S. L. Smith, and K. Simonyan, “High-Performance Large-Scale Image Recognition Without Normalization,” in Proceedings of the 38th International Conference on Machine Learning, M. Meila and T. Zhang, Eds., in Proceedings of Machine Learning Research, vol. 139. PMLR, 2021, pp. 1059–1071. [Online]. Available: https://proceedings.mlr.press/v139/brock21a.html
D. Singh, V. Kumar, and M. Kaur, “Densely connected convolutional networks-based COVID-19 screening model,” pp. 3044–3051, 2021.
N. Radwan, “Leveraging Sparse and Dense Features for Reliable State Estimation in Urban Environments,” 2019. doi: 10.6094/UNIFR/149856.
M. Y. Kamil, “A deep learning framework to detect Covid-19 disease via chest X-ray and CT scan images,” Int. J. Electr. Comput. Eng., vol. 11, no. 1, pp. 844–850, 2021, doi: 10.11591/ijece.v11i1.pp844-850.
M. F. F. Mardianto, A. Yoani, S. Soewignjo, I. K. P. K. A. Putra, and D. A. Dewi, “Classification of Pneumonia from Chest X-ray images using Support Vector Machine and Convolutional Neural Network,” Int. J. Adv. Comput. Sci. Appl., vol. 15, no. 6, pp. 1015–1022, 2024, doi: 10.14569/IJACSA.2024.01506104.
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